The behavior of technical devices can frequently be described by models, for example, in the form of equations. Such models frequently include one or more parameters which have to be determined before the model can then be used to describe the technical installation. For example, a relaxation of an open terminal voltage of a battery can be described with the aid of an equation which includes parameters. If the parameters are known or estimated, it is possible, for example, to use the model to determine an open terminal voltage in the completely relaxed state (that is to say after a long time), the open terminal voltage being, in turn, characteristic for a charging state (degree of charge or degree of discharge) of the battery.
In order to determine such parameters of the model, it is customary to measure at least one physical quantity of the technical installation (for example, a battery voltage against time in the above example of a battery) and then to adapt the parameters in such a way that the model describes the measurement as well as possible. Various algorithms are known for adapting the parameters, for example, the least-square algorithm or the least-mean-square algorithm.
In some cases, here, the at least one physical quantity of the technical installation is repeatedly measured. In such cases, it can be desirable to be able to further adapt the parameter or the parameters of the model with each measurement, and not to have to wait until all the measurements are present. Recursive implementations exist for this purpose, for example of the least-square algorithm, but they require a comparatively high outlay on computation, which can entail, for example, a correspondingly high outlay on hardware for the implementation.